Antipsychotic drugs are the mainstay of the treatment of schizophrenia and are valuable in the treatment of bipolar I disorder and treatment-resistant major depressive disorder. The introduction of second-generation antipsychotics [SGAs] over two decades ago profoundly changed mental health care in the U.S. Annual antipsychotic treatment visits more than doubled between 1995 and 2008, a period during which the share of antipsychotics that was SGA rose from 16% to 93%. Medicaid and Medicare bear a large portion of the economic burden associated with the growth in SGA utilization because these payers provide coverage to the majority of mentally ill people in the U.S. Surprisingly little information exists on contributors to this growth - how much is due to antipsychotic overuse (polypharmacy, off-label use), what are the economic implications to payers, or how growth varies geographically or temporally. Information is particularly thin for dually eligible beneficiaries, a population with complex needs and high utilization. The economic burden to public payers may not be limited to the drugs' purchasing costs. Although evidence on the excess metabolic risk associated with some SGAs led to a FDA class warning and calls for improved prescribing practices, antipsychotics identified to have higher risk remain popular in the treatment of people with serious mental illness [SMI]. Importantly, what we do know largely pertains to short-term health impacts of excess metabolic risk; we know little about diabetes and cardiovascular disease that take longer time to develop or about the economic burden to public payers associated with this risk. We propose a program of research that uses longitudinal (2007- 2013) Medicaid and Medicare data for adults enrolled in Medicaid, Medicare, or both programs in six geographically diverse states to fill in these critical evidence gaps. In Aim 1 we assess the contribution of overuse to state and regional trends in antipsychotic utilization and purchasing costs. In Aim 2 we use marginal structural models to quantify the health and economic impacts of cumulative longitudinal antipsychotic utilization in beneficiaries with SMI. In Aim 3 we use microsimulation modeling with inputs from a national survey, Delphi-gathered expert opinion, and Medicaid and Medicare data to quantify the 6-year and lifetime health and economic impacts of strategies aimed at enhancing value of antipsychotic treatment in beneficiaries with SMI. The data and the methodology we adopt permit a multi-faceted assessment of value by empirically combining cohort-based observations, national survey responses, and results from a Delphi process using contemporary statistical and microsimulation modeling approaches.